OP-ICBJ180106 1247..1254 SYMPOSIUM Communications Principles for Inviting Inquiry and Exploration Through Science and Data Visualization Eric Rodenbeck 1 Stamen Design, 2017 Mission Street No. 300, San Francisco, CA 94110, USA From the symposium “Science Through Narrative: Engaging Broad Audiences” presented at the annual meeting of the Society for Integrative and Comparative Biology, January 3–7, 2018 at San Francisco, California. 1 E-mail: erode@stamen.com Synopsis Science, in the popular imagination, is about finding answers to questions. Scientists make discoveries, de- velop theories, and deliver those discoveries and theories to audiences with an interest in the truth as backed up by science. Well-designed data visualization (dataviz), by contrast, can generate and address not only new questions but new kinds of questions. It has the particular quality of allowing its viewers, users, and makers the ability to generate new inquiries, and to put them in a better place to answer them. Dataviz offers esthetic and interactive platforms for discussion and inquiry that can help scientists to both do their work and better communicate their work to broader audiences. Here I will illustrate and examine case studies from multiple points along the rich and varied possibility space that opens up when science and dataviz work together. I will also introduce three communication principles that I have learned from my involvement with hundreds of dataviz projects over the years. Well-designed dataviz can help scientists and those involved with science find ways to navigate the multiple competing interests and priorities inherent in both communication to non-scientists and exploratory data-rich interfaces. Introduction The focus of dataviz can be understood to exist along a spectrum of abstraction, from facts at the most concrete end, to wisdom, knowledge, and even vision as the most aspirational place for dataviz to work (Fig. 1). Each of these kinds of work requires a dif- ferent approach, and each uses a different kind of raw material and has unique characteristics and outputs. Much of what is widely considered dataviz by practitioners from Edward Tufte to Cole Nussbaumer Knaflic focuses exclusively on the Data row in Fig. 1 of this paper. Through our client-facing and research practice at Stamen, we engage in mul- tiple kinds of dataviz approaches across this spec- trum. Much of this work is done for and with scientists across a broad range of fields, from meta- genomics to the study of human emotions. Thinking of dataviz as more than the communication of facts in the clearest way possible to everyone who looks at it is crucial to unlocking the full communication potential of the medium. There is more to dataviz than communicating simply. One example of this is the visualization of com- plex metagenomic data that the scientists at the Banfield Laboratory at the University of California use to analyze new landscapes of genetic diversity. Their work is difficult to explain to lay audiences, due to its complexity. There is a significant gap be- tween how most people think about metagenomic sequencing and what the science involves. Banfield scientists use data visualizations that Stamen built for them for “hypothesis generation and experimentation” (Stamen Design 2016c). This is data visualization for highly skilled and experienced scientists. It requires their personalized experience, knowledge, and judgment in aiding their work, and belongs more properly in the Wisdom row of Fig. 1 than in the Data row. The interfaces are very difficult for lay people to understand, but serve a crucial role in helping the scientists to look at “at all the recov- ered organisms and all metabolic reactions of these organisms simultaneously” (Stamen Design 2016c). Meeting your audience where they are, whether at a very low or very high degree of scientific literacy or Advance Access publication August 10, 2018 � The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Integrative and Comparative Biology Integrative and Comparative Biology, volume 58, number 6, pp. 1247–1254 doi:10.1093/icb/icy105 Society for Integrative and Comparative Biology Deleted Text: The Deleted Text: a–c Deleted Text: e Deleted Text: a–c https://academic.oup.com/ knowledge abstraction, is key to designing interfaces and visualizations that will help achieve your com- munications goals. What works in one row will likely not work as well in another. The intersection of science, data, and the Internet We are living through an astonishing transformation in the amount and availability of data to people ev- erywhere, both inside and outside of academic insti- tutions. This transition is changing the way that science is done and communicated. As an example, consider the 2008 paper “Magnetic alignment in grazing and resting cattle and deer.” Researchers analyzed the position of thousands of graz- ing animals found on Google Earth and found that deer and cattle tend to align their bodies in a north– south direction: “Amazingly, this ubiquitous phenom- enon does not seem to have been noticed by herds- men, ranchers, or hunters” (Begall et al. 2008). Humans have been looking at deer and cattle for a long time, and yet apparently never noticed the di- rection in which they tend to stand. The emergence of fast, free, and easy access to an accurate and often-updated library of satellite imagery enables observers to ask different kinds of questions than have been previously addressable. While scientists don’t yet understand the proximate mechanisms be- hind this behavior, a statistically sufficient sample dataset now exists for other researchers to build on this work. A 2013 paper, also using Google Earth imagery as the source material, found that “mutual distances between individual animals within herds (herd density) affect their N–S preference” (Slaby et al. 2013). In both these projects, the amount of free and easily available data was the key factor in allowing these insights. There are many other areas where the amount of data available has grown dramatically in recent years, and the field of data visualization is emerging as a set of practices around doing, com- municating about, and otherwise dealing with this rapidly changing landscape and possibility space. This paper presents three communication princi- ples and projects drawn from Stamen’s client services practice visualizing scientific data. These principles can be useful for scientists and the broader public with science through data visualization. Principle 1: Public conversations about science are never just about the truth. It’s wise to plan for this, and not shrink from it Big Glass Microphone (Fig. 2) is a commission Stamen received from the Victoria and Albert Fig. 1 Matrix of data visualization abstraction (Stamen Design 2018). 1248 E. Rodenbeck Deleted Text: internet Deleted Text: - Deleted Text: - Museum in London (Stamen Design 2017). Relying on the work of Biondo Bondi and Eileen Martin at Stanford, the Stanford Exploration Project and the School of Earth, Energy & Environmental Sciences, the project uses data from a 5 km long fiber optic cable buried about a meter under the ground at Stanford University. Light shines through the cable, which responds to vibrations in the environment by changing its shape very slightly. The Stanford team has shown that it’s possible to convert the vibrations of the perturbed optical fiber strands into information about the direction and magni- tude of seismic events. We received a 9-min long sample of data from the Stanford team. We visualized the data in an interac- tive, dynamic interface overlaid on a map of the portion of the Stanford Campus under which the cable is buried. The data are split into different fre- quency bands, ranging from 0.6 to 320 Hz. Each of these bands can be turned on and off, and perturba- tions in the light waves are clearly visible as bright spots along the length of the cable. For example, gasoline-powered cars driving on the road above the cable are visible across a range of the frequency bands. Electric cars, which are mostly si- lent to the naked ear, leave a noticeably different signature. Other perturbation sources, like bicycles, pedestrians, seismic activity, construction equipment, and air conditioners, have different profiles, moving at different speeds than cars. Some perturbations don’t move at all in space, but show significant var- iability of vibration across multiple frequency bands. By comparing the location of this pattern to the geographical map, we determined that one of these stationary but highly varied objects was a fountain, burbling outside the Earth Sciences Building. If a fiber optic cable buried under the ground can be used to detect fountains and cars, what else can be used as a sensor? What is equivalent to the Google Earth example above, when this kind of in- formation is as ubiquitous as satellite imagery? What kinds of problems can we use for these sensing ap- paratuses to investigate? What kinds of new ques- tions could they enable? Big Glass Microphone is a datavis provocation, done under the guise of art and design, designed to provoke more questions than it answers. It re- ceived significant press attention, some of which pre- sented the science behind the project in ways that were not quite accurate and which certainly would not withstand peer review. In particular, some of the articles suggested that the cable could pick up and distinguish human speech, which the researchers have emphasized is not the case. Some of these articles introduced the idea that scientific instrumen- tation can be used in ways that it was not originally intended for, that this can result in some interesting new kinds of observations: “The fiber optic loop un- der Stanford’s campus was originally installed last August for seismic research, but the Stanford team, with the help of OptaSense, decided to turn the noise into signals” (Saplakoglu 2017). The project also opened up the idea for people that this data could be everywhere, and “At a larger scale, imagine how valuable this type of data would be for an engineer corralling traffic, or a mobility service sniffing out customers” (Bliss 2017). From Stamen’s perspective, this is a part of the process Fig. 2 Big Glass Mic. Stamen design. Commissioned by the Victoria and Albert Museum (2017). A provocation that uses material from the “Data” row in Fig. 1 as a source for a project in the Wisdom row, in order to evoke a sense of wonder and mythic imagination applied to unseen infrastructure. Communication principles in data visualization 1249 Deleted Text: , Deleted Text: ve Deleted Text: nine Deleted Text: ute Deleted Text:   Deleted Text: hz Deleted Text: Hz Deleted Text: hz Deleted Text: the Deleted Text: that of engaging in public conversation: not everyone gets all the facts right, some articles are outright wrong, and information can be taken out of context. Articles written in the lay press have a significantly lower threshold for veracity than those accepted in peer- reviewed journals, by design. Communication with these outlets require different strategies than those commonly deployed by the scientific press, and dif- ferent strategies are needed to have conversations with those outside the academy. A key part of this strategy is acknowledging that a natural part of a big public conversation is that not everyone who writes about a project will get every detail exactly correct. It’s not important for every journalist to understand every factual argument that a scientist makes in order to start a useful con- versation about science. “You can’t turn a no to a yes without a maybe in between,” Frank Underwood says in House of Cards (Foley 2015). One of the main things I hope to help scientists understand is that they can learn from Frank Underwood in House of Cards when it comes to communicating about their work. The press will always get something “wrong,” from a scientific perspective. The magnetic alignment projects at the beginning of this article have received widespread press coverage. The 2013 paper occa- sioned an article (Bates 2013) about both of the projects in Wired magazine titled “Cow Compass Points the Way North.” It’s a goofy, catchy title about a complex topic involving real science and sophisticated research—which is exactly the point. It’s not entirely accurate. But it’s in WIRED. The public is engaged with the work. From a commu- nications perspective, that’s more important than whether they get every single detail about the proj- ect correct the first time. This work, though it di- rectly engages with data from a fiber optic cable and might seem to belong in the Data row of Fig. 1, is more useful to think about as a deliberate treatment of a Data project and as a Wisdom project from a communications perspective. It is intended to evoke a sense of wonder and mythic imagination applied to unseen infrastructure. Principle 2: Data visualization can invite more questions than it answers American Panorama (Fig. 3) is an interactive atlas of American History, designed and built by Stamen (Stamen Design 2016a), and commissioned by the University of Richmond’s Digital Scholarship Lab (DSL). The project uses maps and data visualizations to enable discussion of, and citation of, spatial relationships in different historical contexts. Built by and for expert historians, this project and the principle that informs it also belong more in the Knowledge row of Fig. 1 than in the Facts row. One of the maps, Foreign Born, displays the num- ber of Americans counted in each census that were born outside the United States, subdivided into counties. Viewers can see that in 1870 in San Francisco close to half the population was born out- side the country, with the largest numbers of this group coming from Ireland, Germany, and China. These numbers and proportions remain relatively stable through the 1900 census for San Francisco. Note that each link takes you to a different state of the map, an important detail when citing these maps. In 1910, Chinese immigrants, who were for several decades one of the top three foreign born groups in San Francisco, suddenly disappear from the map. The same thing occurs in 1920, 1930, and 1940. There appear to be no Chinese at all. We see them reappear in much-reduced numbers in the 1950 census. The 1960 census groups all Americans of Asian descent into Asia (unspecified). Chinese are listed as the biggest group of foreign born Americans in San Francisco in 1970, where they remain until the 2010 census, which is the most recent as of this writing. We know that Chinatown in San Francisco was an active site of Chinese activity during these years. It made no sense that the data showed zero Chinese born Americans during this time. We therefore thought there was a bug in our code. Perhaps we’d spelled something wrong in the latest compile. But when we looked at the code, everything checked out. We went in and looked at the data: the row for China was empty in the data we’d received from the DSL. We finally asked our clients at the University for clarification. The Chinese Exclusion Act of 1882 (Dunigan 2017) not only severely re- stricted immigration from countries like China. The Act also forbade non-white foreign born people from being counted in the census. They therefore are literally off the map. The scholars at the DSL asked us to remain open to the possibilities of letting project viewers ask questions of the material. We decided together to leave the gap in the data in the project, as a way to invite inquiry into the material. This was deemed more aligned with the project’s goals as a tool for researchers than to explain every aspect of what the data showed. Sometimes (as in this instance) blank spaces on maps are as important as the parts that are filled in. Sometimes noise is as interesting as signal. 1250 E. Rodenbeck Deleted Text: as Deleted Text: ) Deleted Text: Census Deleted Text: Digital Scholarship Lab Principle 3: Data visualization communication is never context-free. There’s no neutral or correct way to do this work We worked with behavioral scientists Paul and Eve Ekman on The Atlas of Emotions (Fig. 4), commis- sioned by the Dalai Lama. His Holiness and Paul have written several books together about emotions, bringing their differing world views to bear on the subject of emotions to the benefit of both. The Dalai Lama, for example, learned about the concept of mood, an emotional state that causes people to in- terpret various events through the lens of an emo- tion that may not be the appropriate one for the task at hand. This was a new notion for him because, in Tibetan Buddhism, there is no concept for a bad mood (Lama and Ekman 2009). And Paul learned about the Tibetan concept of attachment as a kind of fulcrum point between emotional aversion and Fig. 3 American Panorama: foreign born. Census figures for foreign born population of San Francisco in 1890 (above) and 1910 (below). Stamen Design. Commissioned by the DSL at the University of Richmond (2016). Built by and for expert historians, this project and the principle that informs it also belong more in the Knowledge row of Fig. 1 than in the Facts row. Communication principles in data visualization 1251 emotional attraction. The Dalai Lama knows that, while he can speak to a certain kind of audience whose disposition might lead them to listen to what he has to say, others, perhaps those more scientif- ically minded, might be suspicious of the message a Buddhist monk might bring. The Dalai Lama there- fore asked Paul to design for him an atlas of what science knows about how emotions work to address this need. Paul asked us to help him design and build it. This work belongs in the Vision row of Fig. 1, though the principle that informs it can be equally well-applied to any of the rows in that figure. Paul’s response was to design a survey to uncover the consensus among scientists who study emotion, and what they disagree about. According to the sur- vey, scientists agree that all humans share five emo- tions: anger, fear, sadness, disgust, and enjoyment. We then worked with Paul and Eve to design a vi- sual representation of what these data showed, draw- ing on these scientists’ understanding of the structure and nature of human emotions. The project identifies emotional states within each primary emotion, organized by their felt intensity. Among the multiple states of anger, for example, annoyance is felt only at the lower levels of intensity. Fig. 4 The atlas of emotions. Each emotion presented as an independent continent (above) and the states of Enjoyment displayed along with their attendand Actions (below). Stamen design. Commissioned by the Lama and Ekman (2016). This work belongs in the Vision row of Fig. 1, though the principle that informs it can be equally well-applied to any of the rows in that figure. 1252 E. Rodenbeck Deleted Text: Figure Exasperation and bitterness, two other states of an- ger, bridge very low levels of intensity and very high levels of intensity. Fury, the most intense state of anger, is only ever felt at the highest levels. You cannot be slightly furious. Paul had never looked at his work this way before, which was a surprise to both him and us. This re- nowned scientist, whose work laid the foundation for the modern scientific understanding of emotions, had never taken the time to count or map the rela- tive intensities of the different emotional states he’d been studying for 60 years. Far from indicating a gap in Paul’s work, what we feel that this demonstrates is an powerful example of an opportunity for designers and scientists to work together to help bring a new level of visual thinking and accessibility to the vital work of science. Paul later commented that this col- laboration was . . . wasn’t just about discovering things I didn’t know about my own research . . . I also learned things that I didn’t think it was possible to know about my work. (Stamen Design 2016b) The project was designed for an English-speaking, Western audience, as the Dalai Lama had asked us to do. This included color choices, which happened to be the same colors chosen for the five emotions that live in the character of Riley’s head in the recent Pixar movie Inside Out. We chose green for disgust, blue for sadness, orange for enjoyment, purple for fear, and red for anger. When talking about the project, I often used the example of how red is a symbol of good luck in China to illustrate what we assumed was large vari- ance of opinion on color-emotional correspondence in other parts of the world. Surely the Chinese would have a different color for anger. How could an emo- tion generally associated with negativity also be as- sociated with good luck? To my surprise, when I finally had the chance this year to ask a group of Chinese speakers what color they associated with an- ger, they all said the same thing: red. They also told me that they associate the color blue with sadness, but that this was likely only true in China since the introduction of Elvis Presley’s music. Conclusion In this new world of data ubiquity, scientific and dataviz communication is like any other kind of communication. There are many opportunities avail- able and examples to choose from as you decide how to work with data and communicate with it, both to your scientific peers and to the public. Evaluating dataviz science communication based only on whether the public immediately understands all the important aspects to the science can serve as a bar- rier to more widespread understanding of the work. The continuum of types of dataviz (Fig. 1) can serve as a useful framing device for making decisions about how to communicate in different ways to dif- ferent kinds of audiences. It’s important to note that these principles and data types are by no means de- finitive. As has been discussed in relation to Big Glass Mic, useful results can come from applying the lessons from one technique to a project that would seem to better fit in another. Nevertheless, by considering these principles and actively employ- ing them when communicating about their work to lay audiences, scientists can have a greater impact on the world than by adhering to the same strict prin- ciples of scientific accuracy that we expect them to deploy in their research. Acknowledgments Special thanks are due to Martin Karrenbach and John Williams at Optasense for their support on the Big Glass Mic Project and to the Society for Integrative and Comparative Biology for the oppor- tunity to publish this manuscript, and especially to Sara ElShafie for inviting me to participate in this community (http://sicb.org/meetings/2018/abstracts/ correctsymposia.php). References Bates M. 2013 Cow compass points the way north. Wired Magazine (https://www.wired.com/2013/11/cow-compass- points-the-way-north/). Begall S, Cerveny J, Neef J, Vojtech O, Burda H. 2008. Magnetic alignment in grazing and resting cattle and deer. 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